Comparative Analysis of Random Forest and Logistic Regression for Heart Attack Risk Prediction

Cardiovascular diseases, particularly heart attacks, are leading causes of global mortality, highlighting the need for enhanced early detection and intervention strategies. This study evaluates the effectiveness of two machine learning algorithms—Random Forest (RF) and Logistic Regression (LR)—in predicting heart attack risk using diverse patient data sets. The focus is on uncovering subtle patterns and risk factors that traditional methods may overlook, while also assessing the accuracy and performance of both models. A critical aspect of the study is the interpretability of these algorithms, addressing a significant gap in current research. Additionally, the issue of dataset imbalance, which is prevalent in medical data, is examined, and solutions are proposed to improve model reliability in real-world applications. These findings contribute to the discourse on optimizing machine learning in healthcare, advocating for tailored approaches that balance predictive power with interpretability. By analyzing the strengths and weaknesses of RF and LR in heart attack prediction, this study aims to provide valuable insights for clinicians and researchers, ultimately enhancing decision-making processes in cardiovascular care and interventions.